The Generalized Estimating Equations in the Past Ten Years: An Overview and A Biomedical Application

The Generalized Estimating Equations (GEE) proposed by Liang and Zeger (1986) have found considerable attention in the last years and several extensions have been proposed. This paper will give a more intuitive description how GEE have been developed during the last years. Additionally we will describe the advantages and disadvantages of the different parametrisations that have been proposed in the literature. We will also give a brief review of the literature available on this topic. [ Published in: Biometrical Journal 40 (2), 115-139

Regression games

The solution of a TU cooperative game can be a distribution of the value of the grand coalition, i.e. it can be a distribution of the payo (utility) all the players together achieve. In a regression model, the evaluation of the explanatory variables can be a distribution of the overall t, i.e. the t of the model every regressor variable is involved. Furthermore, we can take regression models as TU cooperative games where the explanatory (regressor) variables are the players. In this paper we introduce the class of regression games, characterize it and apply the Shapley value to evaluating the explanatory variables in regression models. In order to support our approach we consider Young (1985)'s axiomatization of the Shapley value, and conclude that the Shapley value is a reasonable tool to evaluate the explanatory variables of regression models

A Gene's Ability to Buffer Variation Is Predicted by Its Fitness Contribution and Genetic Interactions

BACKGROUND: Many single-gene knockouts result in increased phenotypic (e.g., morphological) variability among the mutant's offspring. This has been interpreted as an intrinsic ability of genes to buffer genetic and environmental variation. A phenotypic capacitor is a gene that appears to mask phenotypic variation: when knocked out, the offspring shows more variability than the wild type. Theory predicts that this phenotypic potential should be correlated with a gene's knockout fitness and its number of negative genetic interactions. Based on experimentally measured phenotypic capacity, it was suggested that knockout fitness was unimportant, but that phenotypic capacitors tend to be hubs in genetic and physical interaction networks. METHODOLOGY/PRINCIPAL FINDINGS: We re-analyse the available experimental data in a combined model, which includes knockout fitness and network parameters as well as expression level and protein length as predictors of phenotypic potential. Contrary to previous conclusions, we find that the strongest predictor is in fact haploid knockout fitness (responsible for 9% of the variation in phenotypic potential), with an additional contribution from the genetic interaction network (5%); once these two factors are taken into account, protein-protein interactions do not make any additional contribution to the variation in phenotypic potential. CONCLUSIONS/SIGNIFICANCE: We conclude that phenotypic potential is not a mysterious "emergent" property of cellular networks. Instead, it is very simply determined by the overall fitness reduction of the organism (which in its compromised state can no longer compensate for multiple factors that contribute to phenotypic variation), and by the number (and presumably nature) of genetic interactions of the knocked-out gene. In this light, Hsp90, the prototypical phenotypic capacitor, may not be representative: typical phenotypic capacitors are not direct "buffers" of variation, but are simply genes encoding central cellular functions

Witnessing Violence Toward Siblings: An Understudied but Potent Form of Early Adversity

Research on the consequences of witnessing domestic violence has focused on inter-adult violence and most specifically on violence toward mothers. The potential consequences of witnessing violence to siblings have been almost entirely overlooked. Based on clinical experience we sought to test the hypothesis that witnessing violence toward siblings would be as consequential as witnessing violence toward mothers. The community sample consisted of unmedicated, right-handed, young adults who had siblings (n = 1,412; 62.7% female; 21.8±2.1 years of age). History of witnessing threats or assaults to mothers, fathers and siblings, exposure to parental and sibling verbal abuse and physical abuse, sexual abuse and sociodemographic factors were assessed by self-report. Symptoms of depression, anxiety, somatization, anger-hostility, dissociation and ‘limbic irritability’ were assessed by rating scales. Data were analyzed by multiple regression, with techniques to gauge relative importance; logistic regression to assess adjusted odds ratios for clinically-significant ratings; and random forest regression using conditional trees. Subjects reported witnessing violence to siblings slightly more often than witnessing violence to mothers (22% vs 21%), which overlapped by 51–54%. Witnessing violence toward siblings was associated with significant effects on all ratings. Witnessing violence toward mother was not associated with significant effects on any scale in these models. Measures of the relative importance of witnessing violence to siblings were many fold greater than measures of importance for witnessing violence towards mothers or fathers. Mediation and structural equation models showed that effects of witnessing violence toward mothers or fathers were predominantly indirect and mediated by changes in maternal behavior. The effects of witnessing violence toward siblings were more direct. These findings suggest that greater attention be given to the effects of witnessing aggression toward siblings in studies of domestic violence, abuse and early adversity

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Understanding the interplay between social and spatial behaviour

According to personality psychology, personality traits determine many aspects of human behaviour. However, validating this insight in large groups has been challenging so far, due to the scarcity of multi-channel data. Here, we focus on the relationship between mobility and social behaviour by analysing trajectories and mobile phone interactions of ∼1000 individuals from two high-resolution longitudinal datasets. We identify a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. We show that different individuals balance the exploration-exploitation trade-off in different ways and we explain part of the variability in the data by the big five personality traits. We point out that, in both realms, extraversion correlates with the attitude towards exploration and routine diversity, while neuroticism and openness account for the tendency to evolve routine over long time-scales. We find no evidence for the existence of classes of individuals across the spatio-social domains. Our results bridge the fields of human geography, sociology and personality psychology and can help improve current models of mobility and tie formation